In 2011, TIME Magazine named collaborative consumption (or the sharing economy as it is often called) as one of the top 10 ideas that will change the world.

Four years on, this prediction seems to be holding true. The number of companies operating in the sharing economy is rising rapidly in the transport sector alone, and includes household names such as Uber, BlaBlaCar, Lyft, Zipcar, etc. The public seems to be embracing this phenomenon as we witness users flocking to join these platforms across the globe.

Given that a typical car lies idle some 23 hours a day, car owners are investing in something that they barely use, so this untapped potential is at the heart of the sharing economy in personal transportation. With automated, self-driving cars only around the corner (and some precursor components already in the market in the form of adaptive cruise control, lane assist and self-parking), we decided to look at how combining the shared economy aspect (shared vehicles) with developing technology (automated vehicles) can be applied today, by asking “What if all conventional cars in a city were replaced by a fleet of shared self-driving vehicles”?

The results of this exercise were very interesting.

We carried out a simulation on a representation of the street network of the city of Lisbon, using origin and destination data derived from a fine-grained database of trips on the basis of a detailed travel survey. Trips were allocated to different modes: walking, shared self-driving vehicles or high-capacity public transport. We set a constraint that all trips should take at most 5 minutes longer than today’s car trips take for all scenarios, and assumed all trips are done by shared vehicles and none by buses or private cars. We also modelled a scenario which included high-capacity public transport (Metro in the case of Lisbon).

We modelled two different car-sharing concepts, “TaxiBots”, a term we coined for self-driving vehicles shared simultaneously by several passengers (i.e. ride sharing), and “AutoVots”, cars which pick-up and drop-off single passengers sequentially (car sharing).

For the different scenarios we measured the number of cars, kilometres travelled, impacts on congestion and impacts on parking space.

The results indicate that shared self-driving fleets can deliver the same mobility as today with significantly fewer cars. In a city serviced by ride-sharing TaxiBots and a good underground system, 90% of cars could be removed from the city.

Even in the scenario that least reduces the number of cars (AutoVots without underground), nearly half of all cars could be removed without impacting the level of service. Note: TaxiBots replace more cars than AutoVots since the latter require more vehicles and much more re-positioning travel to deliver the same level of service.

Even at peak hours, only about one third (35%) of today’s cars would be on the roads (TaxiBots with underground), without reducing overall mobility.

No matter what the scenario, on-street parking spots could be totally removed with a fleet of shared self-driving cars, allowing in a medium-sized European city such as Lisbon, reallocating 1.5 million square metres to other public uses. This equates to almost 20% of the surface of kerb-to-kerb street area (or 210 football pitches!)

These findings suggest that shared self-driving fleets could significantly reduce congestion. In terms of environmental impact, only 2% more vehicles would be needed for a fleet of cleaner, electric, shared self-driving vehicles, to compensate for reduced range and battery charging time.

So what are the policy insights from this study?

The impact of self-driving shared fleets is significant but is sensitive to policy choices and deployment scenarios. Transport policies can influence the type and size of the fleet, the mix between traditional public transport and shared vehicles and, ultimately, the amount of car travel, congestion and emissions in the city. For small and medium-sized cities it is conceivable that a shared fleet of self-driving vehicles could completely obviate the need for traditional public transport.

Actively managing freed capacity and space is still necessary to lock in benefits. Shared vehicle fleets free up a significant amount of space in the city. However prior experience indicates that this space must be pro-actively managed in order to lock in benefits. Management strategies could include restricting access to this space by allocating it to bicycle tracks or enlarging sidewalks, or also to commercial or recreational uses, as well as to delivery bays. For example, freed-up space in off-street parking could be used for logistics distribution centres.

Road safety will likely improve; environmental benefits will depend on vehicle technology. Despite increases in overall levels of car travel, the deployment of large-scale self-driving vehicle fleets will likely reduce crashes and crash severity. At the same time, environmental impacts are still tied to per-kilometre emissions and thus will be dependent on the penetration of more fuel efficient and less polluting technologies.

Public transport, taxi operations and urban transport governance will have to adapt. The deployment of self-driving and shared fleets in an urban context will directly compete with the way in which taxi and public transport services are currently organised. These fleets might effectively become a new form of low capacity/high quality public transport. Labour issues will be significant but there is no reason why public transport operators or taxi companies could not take an active role in delivering these services.